MCScheduling 1.0
Set of Algorithms for Solving Mixed-Criticality Scheduling
Packages
Here are the packages with brief descriptions (if available):
MCScheduling.ClairvoyantEDFContains the algorithm to solve mixed-criticality scheduling problems that is an adaptation of a clairvoyant non-preemptive scheduling algorithm by Cecilia Ekelin
MCScheduling.GeneticAlgorithmContains the implementation of a (generic) genetic algorithm that may be used to solve different kind of optimization problems
MCScheduling.MixedCriticalityContains the representation of Mixed-Criticality Job, Mixed-Criticality Instance, several methods for their random generation, and mainly few algorithms to solve Mixed-Criticality Scheduling Problem
MCScheduling.MixedCriticality.CEDFContains the implementation of a mixed-criticality solver based on the clairvoyant non-preemptive earliest deadline first algorithm
MCScheduling.MixedCriticality.DPContains the implementation of a mixed-criticality solver based on the dynamic programming algorithm (similar to the one solving the traveling salesman problem)
MCScheduling.MixedCriticality.GAContains the implementation of a mixed-criticality solver based on the genetic algorithm
MCScheduling.MixedCriticality.MIPContains the implementation of a mixed-criticality solver based on the branch and bound algorithm provided by Gurobi 4.5 library
MCScheduling.MixedCriticality.SAContains the implementation of a mixed-criticality solver based on the simulated annealing algorithm
MCScheduling.SimulatedAnnealingContains the implementation of a (generic) simulated annealing algorithm that may be used to solve different kind of optimization problems
MCScheduling.UtilsContains two pseudo-random number generator implementations; one with uniform distribution (based on famous Mersenne Twister) and the other one with exponential distribution
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